AI-103 Implement Generative AI And Agentic Solutions Practice Test 6 — 15 Questions

Question 1

You are building an agentic application that uses function calling. The model frequently hallucinates arguments for complex tool schemas. Which technique should you apply to improve function calling reliability?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enhance the JSON schema with detailed parameter descriptions, enum constraints, and strict schema validation settings.. Providing clear docstrings, descriptions for every parameter in the JSON schema, and strict mode (if supported) guides the model to populate correct arguments.

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Convert all tool arguments into unstructured natural language text.

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Remove all parameter descriptions from the tool schema to reduce prompt token count.

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Set the model frequency penalty to high values.

Question 2

You are designing an enterprise RAG solution using Azure AI Search and Azure OpenAI. You need to ensure data security and compliance across different user permission tiers. Which TWO actions should you implement? (Choose two.)

Accepted Answer

Ensure all data transfers between Azure AI Search and Azure OpenAI utilize TLS 1.2 or higher.. Security trimming in Azure AI Search ensures users only retrieve documents they have access to, and encrypted connections protect data in transit.

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Store unencrypted user authentication tokens directly inside the embedding vector metadata.

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Grant every user application admin privileges to bypass retrieval latency checks.

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Disable managed identities and store plaintext connection strings in the application source code.

Question 3

You are implementing a multi-turn conversational agent in Azure AI Foundry. As the conversation grows longer, API latency and costs increase significantly. Which strategy should you implement to manage the conversation history effectively?

Accepted Answer

Implement a sliding window history buffer combined with periodic summarization of older dialogue turns.. Summarizing older conversation turns or maintaining a sliding window of recent messages keeps token counts manageable.

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Increase max_tokens to accommodate infinite history growth.

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Disable the chat history array entirely and treat every turn as a standalone prompt.

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Truncate the entire conversation history after every single user turn.

Question 4

You are configuring prompt engineering practices for a generative AI application in Azure AI Foundry. Which THREE practices should you follow to ensure high output quality and reliability? (Choose three.)

Accepted Answer

Specify the desired output format explicitly (e.g., JSON schema or markdown tables).. Effective prompt engineering involves clear separation of context/instructions, few-shot examples, and specifying output formats.

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Embed all system instructions inside the user prompt string without using system roles.

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Rely entirely on vague, implicit instructions to give the model creative freedom.

Question 5

You are troubleshooting an Azure OpenAI deployment in Azure AI Foundry where agents are failing to complete tasks due to excessive timeouts. Which THREE diagnostic steps should you take? (Choose three.)

Accepted Answer

Check if the tokens-per-minute (TPM) quota is throttling requests, causing queued delays.. Troubleshooting agent timeouts involves examining token limits, network latency, and model complexity.

Answer

Double the model temperature to 2.0 to accelerate reasoning speed.

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Delete all vector indices to force local memory processing.

Question 6

You are building an autonomous agentic workflow using Microsoft Foundry and Semantic Kernel. You need to ensure the agent operates safely and adheres to human oversight requirements. Which THREE design patterns should you apply? (Choose three.)

Accepted Answer

Implement human-in-the-loop approval gates before the agent executes high-impact tool actions such as database updates or financial transactions.. Human-in-the-loop approval gates, transaction boundaries, and rate limiting ensure safe agentic execution.

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Grant the agent root-level administrative access to all cloud infrastructure services.

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Allow the agent unlimited recursive tool execution without loop detection.

Question 7

When designing a Responsible AI evaluation workflow in Azure AI Foundry, which TWO metrics are specifically used to assess content safety and potential harms? (Choose two.)

Accepted Answer

Hate speech and unfairness detection. Violence, sexual, hate, and self-harm detection along with protected material detection are core safety metrics in Azure AI Foundry.

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Word count frequency

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BLEU machine translation score

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Model parameter count

Question 8

Which TWO factors directly influence the embedding quality and retrieval relevance in an Azure AI Search vector retrieval pipeline? (Choose two.)

Accepted Answer

The choice of embedding model (e.g., text-embedding-3-large vs text-embedding-3-small) and its dimensionality.. Embedding model choice and chunking strategy are primary determinants of vector retrieval quality.

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The background color of the source documents in blob storage.

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The font family used in the original PDF documents.

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The client browser version used by the end user.

Question 9

You are optimizing an agentic workflow in Azure AI Foundry. The agent needs to solve complex reasoning problems by breaking them down into distinct sub-tasks, executing them iteratively, and reflecting on intermediate results. Which THREE architectural frameworks or patterns support this behavior? (Choose three.)

Accepted Answer

Reflection pattern where the agent critiques its own intermediate outputs and refines them.. Chain-of-thought, ReAct (Reason+Act), and plan-and-solve are foundational agentic reasoning patterns.

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Static hardcoded IF-ELSE decision trees without model inference.

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Single-turn stateless completion calls with temperature set to zero.

Question 10

You are developing a customer support agent using Microsoft Foundry. The agent needs to call a custom weather API tool when a user asks about local conditions. How should you define this capability in the agent configuration?

Accepted Answer

Register a custom function tool by providing an OpenAPI schema definition that describes the weather API endpoints, parameters, and expected responses.. In Microsoft Foundry and Azure AI Agent Service, you configure custom tools by defining them as OpenAPI specifications or functions that the model can invoke via function calling capabilities.

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Upload the compiled C# library containing the API client code directly into the agent code interpreter workspace.

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Add the weather API endpoint URL as a grounding data source under the vector store settings.

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Configure a managed data connection pointing to the weather API URL within Azure AI Search.

Question 11

When configuring Azure OpenAI Custom Model Evaluation in Azure AI Foundry, which TWO metrics can be computed using LLM-as-a-judge evaluators? (Choose two.)

Accepted Answer

Coherence. Coherence and relevance are standard LLM-as-a-judge evaluation metrics in Azure AI Foundry.

Answer

CPU utilization percentage

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DNS propagation delay

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Disk input/output operations per second (IOPS)

Question 12

You are implementing Retrieval-Augmented Generation (RAG) in Azure AI Foundry using Azure AI Search as the grounding data source. Which indexing feature should you enable to ensure the system can perform semantic ranking alongside traditional keyword search?

Accepted Answer

Semantic Search. Semantic ranking uses advanced language models to re-rank top search results, significantly improving the relevance of retrieved chunks for RAG pipelines.

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Fuzzy Search

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Lucene Standard Analyzer

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Vector Normalization

Question 13

You are designing an agentic workflow in Azure AI Agent Service where multiple specialized agents need to collaborate to solve complex, multi-step financial analysis tasks. One agent manages the workflow state and decides which sub-agent to invoke next. What architectural pattern is best suited for this implementation?

Accepted Answer

Orchestrator-workers pattern, where a central agent dynamically assigns sub-tasks to specialized worker agents based on intermediate results.. The orchestrator-workers (or router) pattern uses a central orchestrator agent to manage state, evaluate progress, and delegate tasks to specialized worker agents.

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Single monolithic prompt pattern, where all instructions are combined into one massive system prompt without multi-agent handoffs.

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Map-reduce batch processing pattern, where all sub-agents execute independently in parallel without communication.

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Static sequential pipeline, where agents execute strictly in a predefined linear order without conditional branching.

Question 14

You are configuring content safety filters in Azure AI Foundry for a public-facing generative AI application. You need to ensure that the application detects and blocks potential jailbreak attempts and protected material text. Which component should you configure?

Accepted Answer

Azure AI Content Safety text moderation service with jailbreak and protected material detection enabled.. Azure AI Content Safety provides specialized detectors for hate speech, sexual content, violence, self-harm, jailbreak attacks, and protected material detection.

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Azure Key Vault access policies restricting model endpoint tokens.

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Azure API Management rate-limiting policies configured to drop requests containing long prompts.

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Azure Monitor activity log alerts for anomalous token usage.

Question 15

You are configuring a code interpreter tool for an AI agent in Azure AI Agent Service. The agent needs to analyze a CSV dataset uploaded by a user. How does the agent execute this task securely?

Accepted Answer

The agent generates Python code to analyze the CSV and executes it inside a secure, isolated sandbox environment managed by the service.. The code interpreter runs in a secure, sandboxed execution environment where it dynamically writes and executes Python code to process data and generate files.

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The agent compiles the CSV data into C# binaries and runs them on the host Azure App Service instance.

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The agent sends the raw CSV data to an external public third-party execution API.

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The agent translates the CSV data into SQL queries and executes them directly against the primary production database.